Methods employing non-coding rna expression assays

ABSTRACT

There is disclosed a method comprising the steps of: carrying out a plurality of expression assays, each expression assay comprising the steps of: carrying out an intervention on a biological system, measuring an expression profile of non-coding RNAs in the biological system resulting from the intervention, and storing an expression data set derived from the measured expression profile, the said expression assays concerning either or both a plurality of different interventions and a plurality of different biological systems; and analysing the resulting expression data sets to determine correlations between the effect on the expression profile of non-coding RNAs of the respective intervention in groups of two or more expression assays concerning either or both different interventions or different biological systems.

FIELD OF THE INVENTION

The invention concerns methods employing non-coding RNA expressionassays. Embodiments of the invention addresses problems including, butnot limited to, determining similarities in the mechanism by which twoor more interventions affect biological systems, identifying candidatetherapeutic applications of test agents and identifying new applicationsof therapeutic agents which have previously been the subject of clinicaltrials in respect of one or more indications.

BACKGROUND TO THE INVENTION

Issues concerning the invention will now be discussed with reference toapplications of microRNA (miRNA) expression assays, however, theinvention may employ expression assays concerning other non-coding RNAmolecules.

miRNAs are single-stranded RNA molecules having a length of around 21 to23 nucleotides. miRNAs were first described by Victor Ambros in 1993 andsince then over 2,000 papers on have been published on the subject ofmiRNAs. There are predicted to be about 1,000 miRNAs in humans of whicharound 600 have been described and experimentally validated to date,although some estimates place the figure at tens of thousands. However,a recent report, which sought to produce an expression atlas of miRNA invarious human and rodent tissues and cell lines, reported that around300 miRNAs accounted for 97% of all detected miRNAs.

miRNA is not translated into protein but instead regulates theexpression of one or more other genes. Known biology currently showsthat microRNAs target particular individual messenger RNAs (mRNAs) orgroups of mRNAs, thereby preventing their translation or, lessfrequently, accelerating mRNA degradation. The mature single strandedmiRNA molecule complexes with the RNA-Induced Silencing Complex (RISC)protein and binds to a partially complementary sequence within the 3′untranslated region (3′-UTR) of the protein coding mRNA from its targetgene. Further proteins are recruited to form a silencing complex and theexpression of the target gene product is repressed by a mechanism thatblocks the translation of the mRNA.

Although much remains to be discovered about the biology of miRNAs andthe composition and mechanism of the silencing complex it is apparentthat miRNAs are involved in the regulation of many genes. MiRNAs arethought to regulate as many as 30% of all genes (Xie et al, 2005) at thetranslational level. An miRNA can regulate multiple genes and each genecan be regulated by multiple miRNAs. Tissue-specific expression ofmiRNAs is thought to guide commitment of cells to differentiate and/oractively maintain tissue identity. This wide-ranging influence andinterplay between different miRNAs suggests that deregulated expressionof a single miRNA or small sub-set of miRNAs may result in complexdisease traits (Lim et al, 2005, Nature). More than 50% of known humanmiRNAs reside in genomic regions prone to alteration in cancer cells(Calin et al, 2004 PNAS, 101, 299-3004). Not surprisingly, theexpression pattern of miRNAs change in cancer and other disease states.This information has begun to be used to classify and stage cancers,reveal biomarkers for prognosis and response and provide a criticaldeterminant to guide therapeutic intervention, explain chemosensitivityand inform the mechanisms of chemoresistance by allowing the definitionof specific miRNA expression patterns in cancer stem cells.

Applications of miRNAs to research and the development of possible newtherapeutics have typically resulted from detailed and time consuminganalysis of the mechanisms by which miRNA expression and processing isregulated and the mechanisms by which specific miRNAs regulate mRNAtranslation. Specific drug targets have been identified and research inconnection with these drug targets in ongoing. However, althoughthorough, this research paradigm is time consuming and expensive.

Thus, the invention aims to provide alternative methods for discoveringpractical applications of interventions, such as the administration of atherapeutic agent, which do not require a detailed understanding of themechanism of action of the intervention or the identification of aspecific drug target. Some embodiments of the invention address theproblem of determining new indications for known therapeutic entities orpredicting pharmacological properties of test agents, such as aspects oftheir toxicological profile.

SUMMARY OF THE INVENTION

According to the present invention there is provided a method comprisingthe steps of:

-   (1) carrying out a plurality of expression assays, each expression    assay comprising the steps of: carrying out an intervention on a    biological system, measuring an expression profile of non-coding    RNAs in the biological system resulting from the intervention, and    storing an expression data set derived from the measured expression    profile, the said expression assays concerning either or both a    plurality of different interventions and a plurality of different    biological systems; and-   (2) analysing the resulting expression data sets to determine    correlations between the effect on the expression profile of    non-coding RNAs of the respective intervention in groups of two or    more expression assays concerning either or both different    interventions or different biological systems.

By analysing expression data sets to determine similarities between theeffect of an intervention on the expression profile of non-coding RNAsin groups of two or more expression assays, which differ in terms ofeither or both the intervention which was carried out and the biologicalsystem upon which the intervention was carried out, correlations may bedetermined without it being necessary to determine the mechanism bywhich one or more interventions affect the expression profile ofnon-coding RNAs in one or more biological systems.

Thus, where a second intervention is found to have an effect on theexpression profile of non-coding RNAs which is correlated with theeffect of a first intervention which is of known therapeutic relevance,the second intervention can be treated as a candidate for the same or asimilar therapeutic application. There is at least some possibility thatthe first and second interventions will have the same, a similar, or arelated mechanism of action. This methodology is in direct contrast toknown strategies for discovering therapeutic interventions, in which aspecific target (such as a protein, nucleic acid or liquid molecule) isidentified, and analysed, the biology of the target is studied in depth,and therapeutic interventions suitable to modulate one or moreactivities of the target are developed by rational and/or combinatorialmethods.

One or more (and optionally all) said interventions may comprise theapplication of one or more test agent to a biological system, eithersimultaneously or sequentially. The one or more test agent may be achemical entity, for example, a molecule having a molecular weight ofless than 2,000 Daltons, less than 1,000 Daltons or less than 500Daltons. The chemical entity may be non-polymeric. The one or more testentity may be a biological entity, for example, a biologicalmacromolecule, such as a lipid, an oligonucleotide, or a protein (e.g.an enzyme, an antibody, or antibody fragment, humanized antibody orantibody fragment, phage or ribosome displayed protein fragment, or aprion). The biological entity may be a virus or bacteria. Thus, some oreach of the expression assays may measure the effect of a test agent onthe expression profile of non-coding RNAs in a biological system.

One or more said test agents may be a therapeutic agent. One or moresaid test agents may be a therapeutic agent having a known applicationto the treatment or prevention of a known condition. One or more saidtest agents may be a therapeutic agent which has been the subject ofclinical trials (whether or not successfully) in relation to one or moreindications. However, one or more (and optionally all) saidinterventions may comprise the application to a biological system of oneor more of a group comprising: ionising radiation, continuously emittedor pulsed electromagnetic radiation (for example, visible light,ultra-violet light, infra-red light), acoustic energy (delivered throughair or through a liquid medium), mechanical intervention (for example,the application of pressure), electricity, changes in temperature,changes in the osmolarity, tonicity or pH of a growth medium, magneticfields, changes in fluid dynamics, and mechanochemical signaltransduction. Thus, at least some interventions may be interventionswhich are known to be deleterious to the biological system. Theexpression profile resulting from such deleterious interventions may beuseful to identify agents which reverse or prevent the deleteriouseffects.

One or more said biological systems may comprise cells, such asmammalian cells, for example, the cells of a human, a rabbit, or arodent (for example a mouse or a rat), or cultured insect, amphibian orfish cell lines. One or more said biological systems may comprise amixture of cell types. The intervention is typically carried out oncultured mammalian cells. The mammalian cells may be stem cells orprogenitor cells. By stem cells we refer to cells which are capable ofself-renewal and differentiation into at least one other specialisedcell type. However, one or more said biological systems may be a wholeorganism, ex-vivo tissue, a synthetic system or transformed cells. Theone or more said biological systems may be transgenic. Cultured cellsmay have synchronous or asynchronous cell cycles. One or more saidinterventions may be an intervention which changes the differentiationor de-differentiation state of a stem cell or progenitor cell, or whichcauses a stem cell or progenitor cell to specialise, or to replicatewhile maintaining the characteristics of a particular cell lineage ordifferentiation state.

The expression assays may be repeated and the expression data sets whichare analysed may be compiled from some or all repeat experiments usingequivalent interventions on equivalent biological systems.

Correlations are typically between the expression of a subset of thenon-coding RNAs in connection with which expression data is stored inthe expression data sets. Correlations between effects on the expressionprofile of non-coding RNAs are typically correlations, which may bepositive or negative, in the change in the expression of one, or a smallnumber of (e.g. two, three, five or fewer than five, or ten or fewerthen ten) non-coding RNAs between two or more expression assays. Apositive correlation may comprise an increase in the expression of oneor more non-coding RNAs in each of two expression assays. A positivecorrelation may comprise a decrease in the expression of one or morenon-coding RNAs in each of two expression assays. A negative correlationmay comprise an increase in the expression of one or more non-codingRNAs in a first expression assay and a decrease in expression of thesame one or more non-coding RNAs in a second expression assay.

In order to determine correlations between the effect on the expressionprofile of non-coding RNAs of a respective intervention, the method mayfurther comprise measuring the expression profile of non-coding RNAs ina suitable control assay, for example a control assay in which therespective intervention is not carried out, or a control assaycomprising measuring the expression profile of non-coding RNAs in abiological system prior to the respective intervention being carriedout. Differences between the expression profile of non-coding RNAs inexpression assays and corresponding control assays may be determined.The stored expression data set may be derived from a measured expressionprofile and an expression profile of a corresponding control assay. Thestep of analysing the resulting expression data may comprise taking intoaccount expression profiles from control assays. However, in someapplications it will not be necessary to carry out control assays. Forexample, if a plurality of interventions are carried out on equivalentbiological systems it may be necessary only to analyse data sets derivedsolely from the expression profiles resulting from each expression assayto determine correlations between the effect on the expression profileof non-coding RNAs of the respective interventions.

It may be that at least some of the said correlations are positivecorrelations, for example, similarities between the effect on theexpression of non-coding RNAs of the respective intervention in groupsof two or more expression assays. The step of analysing the resultingexpression data sets to determine correlations may include the step ofcategorising (for example, clustering or grouping) expression assays onthe basis of similarities between the expression data sets resultingfrom expression assays. Advantageously, this may allow similarities inthe mechanism by which two or more different interventions have aneffect directly or indirectly on the expression profile of non-codingRNAs (typically on the same or equivalent biological systems) to beidentified without a requirement for the nature of the shared mechanismto be understood.

Thus, the method may be a method of determining that two or moreinterventions have similar effects on the expression of one or morenon-coding RNAs. A first intervention may be the application of a firsttherapeutic entity, having at least one known first therapeuticapplication, and the method may be a method of determining that there isa positive correlation between the effect on non-coding RNA expressionof a second intervention, comprising the application of a secondtherapeutic entity. Accordingly, the method may be a method ofdetermining a possible new therapeutic application (the firsttherapeutic application) for a known therapeutic entity (the secondtherapeutic entity). The method may further comprise the test of testingwhether a second intervention is applicable to the treatment of a saidknown first therapeutic application.

The first intervention may be the application of an entity without aknown first therapeutic application, but which is known to havepharmacological and toxicological profiles suitable for deployment as atherapy. Thus, the method may be a method of determining a possible newtherapeutic application of a therapeutic entity which has passedtoxicology trials but failed to be found to be efficacious, or moreefficacious than a control therapeutic entity, in clinical trials.

It may be that at least some of the said correlations are negativecorrelations, for example, it may be determined that two or moreinterventions have opposite effects on the expression of one or morenon-coding RNAs. Advantageously, a negative correlation between theeffect on the expression profile of non-coding RNAs of a firstintervention and a second intervention may indicate that the secondintervention could possibly be useful to reverse one or more effects ofthe second intervention in therapy. Thus, the first intervention may bean intervention which is known to have a deleterious effect on thebiological system, for example, the first intervention may be theapplication of a toxin. In this case, the method may compriseidentifying the second intervention as a candidate for the treatment orprevention of a condition known to be caused by the first intervention.

Thus, a plurality of interventions may comprise the administration of atoxin or a treatment which is deleterious to the biological system. Themethod may therefore be part of a method of determining candidateinterventions (e.g. candidate therapeutic entities) which may treat orprevent a condition known to be causable by one or more otherinterventions. The method may be a part of a method of determiningcandidates to treat or prevent side effects of known therapeuticinterventions (e.g. the application of a therapeutic entity or aradiotherapy).

The method may be a method of predicting one or more aspects of thetoxicity of a test agent, for example, by detecting that the expressionprofile of non-coding RNAs arising from a first intervention ispositively correlated with the expression profile of an interventionwhich is known to have a deleterious effect on the biological system, orpositively correlated with the expression profile of an interventioncomprising the administration of an agent, one or more aspects of thetoxicology of which are known.

The method maybe a method of determining that a first test agent is acandidate agonist or antagonist of a second test agent, or a specifictarget macromolecule, by determining a correlation respectively betweenthe effect on the expression profile of non-coding RNA of the first testagent and the second test agent, or a test agent which is a knownagonist or antagonist of the target molecule.

The method may comprise the step of grouping interventions which havesimilar effects on the expression of non-coding RNAs. The resultingexpression profiles may be useful starting points for further researchto identify further therapeutic entities. The method may be a method ofdetermining changes in an expression profile of non-coding RNAsassociated with a group of interventions, for example, a group oftherapeutic entities. Thus, the method may be a method of determiningthat a chemical or biological entity has a mechanism of action on abiological system which is related to the mechanism of action of anotherchemical or biological entity on a biological system. Groups may beordered in a hierarchy.

Where an intervention is the application of a second test agent to thebiological system and the effect of the application of the second testagent on the non-coding RNA expression profile is found to be correlated(positively or negatively) to the effect of another first test agent onthe biological system, which first test agent is known to be useful forthe treatment or prevention of a first condition, the second test agent,or test agents obtained by modifying the second test agent, may betested for efficacy in the treatment or prevention of the firstcondition, or a condition related to the first condition. Test agentswhich are found to be efficacious for the treatment or prevention of thefirst condition, or a condition related to the first condition, may bedeployed from the treatment or prevention of the relevant condition.

In some embodiments, expression assays are carried out in which the sameintervention, or group of interventions are carried out on a pluralityof different biological systems. Thus, the method may enable thediscovery of correlations between the effects of interventions which arepresent in only some of the plurality of different biological systems.In some embodiments, the plurality of different biological systems arestem cells in different states of differentiation or de-differentiation,for example, different stages of development. Thus, the method mayenable the discovery of correlations between the effects ofinterventions on stem cells in specific states of differentiation orde-differentiation. This information is useful to investigate themechanisms of development and stem cell or progenitor celldifferentiation and de-differentiation. The plurality of differentbiological systems may comprise mammalian cells in different diseasestates. An intervention may be an intervention which causes stem cellsor progenitor cells to differentiate or de-differentiate, or drive theattainment of a specific differentiation state or maintain the stabilityof stem cells or progenitor cells in a particular differentiation state.

The expression profile is related to the expression of at least one, andtypically a plurality of non-coding RNAs, preferably at least 10, ormore preferably at least 100 non-coding RNAs. The expression profile maybe related to the expression of one or more transgenic non-coding RNAsfunctioning as markers. An expression profile may include quantitativeor qualitative measurements of the level of expression of one or morenon-coding RNAs. The level of expression of one or more said non-codingRNA may be determined indirectly via measurements of the amount or levelof activation of a reporter construct, for example, a transgenicreporter construct incorporated into the genome of the biologicalsystem, or maintenance episomally, in a particular biological system.The expression profile is typically related to the amount of one or morenon-coding RNAs which are expressed in at least some circumstances inthe biological system, for example, the steady state or peak amount ofthe one or more non-coding RNAs. However, the expression profile may,for example, be related to the rate of change of expression of one ormore non-coding RNAs. In some embodiments, the expression profiles areobtained using a microarray.

The non-coding RNAs typically include microRNAs (miRNAs) and may includeeither or both miRNA precursors and mature miRNAs. The non-coding RNAsmay comprise one or more of small interfering RNAs (siRNA),piwi-interacting RNA (piRNA), small nuclear RNAs (snRNA), and shorthairpin RNA (shRNA). The non-coding RNAs may be transgenic. Some or allof the RNAs may, for example, be transgenic RNAs which function asreporters of non-coding RNA expression. The non-coding RNAs may beepisomal and the method may include the step of introducing episomal DNAinto the biological system, for example by infection of a biologicalsystem with a virus, wherein the episomal DNA can be transcribed toproduce non-coding RNA which constitute all or part of the profilednon-coding RNA.

Expression profiles may be measured for each non-coding RNA in a groupof non-coding RNAs and the method may comprise identifying individualnon-coding RNAs, or a sub-group of the group of non-coding RNAs, whichhave expression profiles on which a plurality of interventions have acorrelated effect.

The plurality of interventions which have correlated effects may beidentified by the method of the invention, thus enabling bothinterventions which have correlated effects on the expression profile ofa group of non-coding RNAs and the individual non-coding RNAs orsubgroup of non-coding RNAs within the group having expression levelswhich are affected by the plurality of interventions to be identified.

The plurality of interventions which have correlated effects may beinterventions which are previously known to have a related mechanism ofaction, for example, the plurality of interventions may comprise theadministration of agents known or believed to have the same or a similarmechanism of action, for example a class of drugs. Thus, the inventionprovides a method of identifying the individual non-coding RNAs or asubgroup of non-coding RNAs having expression levels affected by theplurality of interventions.

The resulting identified individual non-coding RNAs or identifiedsub-groups of non-coding RNAs may then be selected for use in furtherexpression assays in which the expression profile of a reduced group ofnon-coding RNAs is measured, the reduced group of non-coding RNAsincluding only some of the group of non-coding RNAs, including, oroptionally consisting of, at least the identified individual non-codingRNAs or identified sub-groups of non-coding RNAs. The effect of furtherinterventions on the expression profile of the reduced group ofnon-coding RNAs and correlations between the effect of furtherinterventions on the expression profile of the reduced group ofnon-coding RNAs and the effect of the said plurality of interventions onthe expression profile of the reduced group of non-coding RNAs can bethereby determined. Thus, subsequent assays and tests may employ fewernon-coding RNAs, reducing costs and increasing throughput. For example,a reduced group of non-coding RNAs having expression levels upon which aclass of therapeutic agents have a correlated effect may be used toscreen candidate agents, either to find novel therapeutically usefulagents or to identify new indications for known therapeutic agents.

The relevance of the expression level of a group or sub-group ofnon-coding RNAs to discrimination between the effect of biologicalinterventions may be determined. The method may comprise the step ofranking non-coding RNAs within the group or sub-group depending on theirrelevance to discrimination between the effects of biologicalinterventions. The method may comprise the step of ranking the effect onthe expression of non-coding RNAs in a group or sub-group of non-codingRNAs of a biological intervention, or a group of biologicalinterventions having a correlated effect on the expression of non-codingRNAs. The resulting rankings may be used to identify correlationsbetween the effects of biological interventions.

Correlations may be identified by statistical mathematical methods, forexample, principle component analysis. The effect of a biologicalintervention on the expression of each of a plurality of specificnon-coding RNAs may be allocated one of a group of codes indicative ofproperties of the effect of the biological intervention on theexpression of the respective non-coding RNA. The resulting codes may beanalysed to identify correlations.

The invention also extends to assay apparatus (for example a test kit ora solid phase support having non-coding RNAs immobilised thereto) havingnon-coding RNAs consisting of a said reduced group of non-coding RNAs,obtained by the method of the invention.

DESCRIPTION OF THE DRAWINGS

An example embodiment of the present invention will now be illustratedwith reference to the following Figure in which:

FIG. 1 is a flow diagram of a method according to the invention;

FIG. 2 is a plot of the results from principal component analysis forone biological intervention (a) and for one variable (b);

FIG. 3 is a table giving statistical rankings of 11 miRNAs by theirp-value and q-value; and

FIG. 4 is a plot of data from principal component analysis showing (a) alabelled sub-group of discriminatory miRNAs, and (b) data from fourintervention types showing how the expression data from differentintervention types cluster.

DETAILED DESCRIPTION OF AN EXAMPLE EMBODIMENT

In an example application of the invention, a database of miRNAexpression data sets (being an example of an expression data set derivedfrom a measured non-coding RNA expression profile) is prepared. Withreference to FIG. 1, suitable human cells are cultured 2 by knownmethods and a test agent is administered 4 to the cultured cells. AmiRNA expression profile is then measured 6 using a sample of thetreated cells, at one or more periods of time after the intervention ismade, to determine the expression level of each of a number of miRNAs inthe treated cells.

Two alternative methods for measuring the miRNA expression profiles,microarray analysis and qualitative real-time PCR analysis, are set outbelow.

(1) miRNA Microarray and Data Analysis

Total RNA from drug-treated (n=3) and control treated cells (n=3) areisolated using a column-based kit from Exiqon A/S of Vedbaek, Denmark.Two μg of total RNA from each sample is analysed by miRNA microarray.miRNA microarray analysis including labelling, hybridization, scanning,normalization and data analysis is commercially available from a numberof sources, for example, from Exiqon A/S. Briefly, RNA Quality Controlis performed using Bioanalyser 2100 microfluidics platform (Bioanalyseris a trade mark of Agilent Technologies). Samples are labelled using theComplete Labelling Hyb Kit from Agilent, following the providedinstructions.

(2) Quantitative Real-Time PCR

As with option (1) above, all cellular RNA is extracted using acolumn-based kit from Exiqon and following the manufacturer'sinstructions. Quantification of miRNAs by TaqMan Real-Time PCR iscarried out as described by the manufacturer (Applied Biosystems ofFoster City, Calif., USA). (TaqMan is a trade mark of Roche MolecularSystems, Inc.). Briefly, 10 ng of RNA is used as a template for reversetranscription (RT) using the TaqMan MicroRNA Reverse Transcription Kitand miRNA-specific stem-loop primers (Applied Biosystems). An aliquot(1.5 μl) of the RT product is introduced into 20 μl PCR reactions whichare incubated in 96-well plates on the ABI 7900HT thermocycler (AppliedBiosystems) at 95° C. for 10 min, followed by 40 cycles of 95° C. for 15s and 60° C. for 1 min. Target gene expression is normalized betweendifferent samples based on the values of U48 RNA (a small, non-codingRNA) expression (or GAPDH, if U48 is found to vary with drug treatment).

In each case, the resulting miRNA expression levels are stored as 8expression data sets. A large number of expression assays are preferablycarried out. Typically, many (e.g. hundreds or thousands) of test agentsare introduced to cell cultures and analysed in this way to create adatabase of miRNA expression data.

Once a suitably large database of miRNA expression data sets areavailable, the expression data sets are analysed 10 to determinecorrelations between the effects of each test agent on miRNA expressionand to create hierarchical clusters of test agents which have similareffects on the miRNA expression profiles.

Methods for determining correlations between nucleic acid expressiondata sets are well known to those skilled in the art. For example, onemethod is to import microarray data obtained from Exiqon A/S in the GPRformat into a spreadsheet. (GPR is the data format used by Genepix6software, available from Molecular Devices of Union City, Calif., USA.Genepix is a trade mark of Molecular Devices). Spot intensities for eachmiRNA are analysed against quality control and calibration spotsprovided on the miRNA array (indicated by Genepix6 software as anegative flag). Values with signal intensities below 50 are brought upto 0. For each of the four replicate spots for each miRNA capture probespecies, the median value of the background corrected spot intensity iscalculated and imported into TMeV microarray analysis software whichperforms hierarchical clustering and/or other statistical analysesfamiliar to one skilled in the art. (TMeV is provided by the Dana-FarberCancer Institute, at the URL www.tm4.org).

Alternatively, GRP format expression data may be imported intoGenespring GX software, available from Agilent Technologies. (GenespringGX is a trade mark of Agilent Technologies), normalised to the 75^(th)percentile and then processed using hierarchial clustering and otherstatistical tools built into Genespring GX.

Where positive correlations are found between the effects of two or moretest agents on the expression of one or more miRNAs, this may beindicative that the test agents share the same, or a related, mechanismof action. Thus, test agents which are found to have similar effect onmiRNA expression profile as an agent which is known as a treatment for acondition can be identified 12 as candidates for treatment of the same,or a related condition. This may be useful to facilitate therepositioning of drugs which have already been identified as potentiallyuseful for one therapeutic application. Candidate test agents can betested to determine whether they may be useful for treatment of thesame, or a related condition, or used as the starting point for furtherresearch. For example, they might be modified using rational orcombinatorial design methodologies, a mimetic compound might be preparedand tested and so forth. Candidate test agents can be tested 14 todetermine whether they are suitable for use as therapeutic entities and,if, they are, deployed 16 as therapeutic entities.

It is especially useful to group test agents which have similar effectson the expression of one or more miRNAs as this classification by effecton miRNA expression may be reflected in a similar or related mechanismof action, whether direct or indirect, on miRNA expression levels.

Where negative correlations are identified, one test agent might beidentified as a candidate to prevent, mitigate or obviate one or moreundesirable affects of a further test agent or other intervention. Thus,a test agent which is known to have an opposite effect on the expressionof one or more miRNAs to another test agent which has an undesirableeffect could be considered as a candidate entity for the treatment orprevention of that undesirable effect.

Advantageously, miRNA expression assays are carried out to assess theeffect of a range of interventions, including interventions other thanthe administration of a chemical or biological entity. For example,cells may be treated with ultraviolet light, ionising radiation,acoustic waves and other interventions which are deleterious to thecells. Where a test agent can be identified which has an effect on theexpression of one or more miRNAs which is negatively correlated to theeffect of such interventions, the test agent may be a candidate for thetreatment or prevention of undesirable effects resulting from acorresponding intervention in vivo. This may be useful to identifyagents for the prevention of damage caused by ultraviolet light or asside effects from radiotherapies.

The method can be applied to the high-throughput screening of largenumbers of test agents (e.g. combinatorial libraries of small chemicalentities, peptides, peptidomimetics or polynucleic acids). As newexpression assays are carried out the resulting expression data sets canbe compared against previously stored expression data sets to look forcorrelations between the effects of screened test agents and agentswhich have been previously assayed.

The method is typically best employed using a large database of miRNAexpression data sets. However, for some specific applications it mayonly be necessary to have a small number of miRNA expression data sets,or even one miRNA expression data set, available for comparison with themiRNA expression data set resulting from a new assay. This may berelevant in high-throughput screens to find agents which have an effecton miRNA expression which correlates positively or negatively with aparticular identified effect, for example, the effect of an agent whichis known or suspected as having a significant effect on miRNAexpression.

Thus, the invention is based on a principle that similarities inmechanism of action, and therefore practical applications, of testagents (such as chemical entities and biologics) may be found throughthe comparative analysis of their effects on the expression of miRNAs(and potentially other non-coding RNAs) without it being essential tounderstand the mechanism through which the test agents affect miRNAexpression profiles. This is in direct contrast to conventional drugdiscovery and drug repositioning strategies in which a mechanism ofaction is researched in depth to identify a drug target for use inscreening assays to discover agents which have a desired interactionwith the drug target.

Experimental Findings and their Implications

Using the methods described we have determined that it is possible todetermine potential modes of therapeutic application of interventionsbased on the grouping of miRNA expression data. Furthermore, the methodcan be employed to identify certain miRNAs, having expression levelswhich are indicative of certain therapeutic applications forinterventions being screened. Such indicative miRNAs will enable futureintervention screening to analyse a relatively small group of miRNAexpression levels to identify potential therapeutic applications of theinterventions being screened, and not the entire miRNA library.

An example of using a select small group of miRNAs to determinepotential therapeutic uses for an intervention is given below.

During experiments described here, by way of a control, a group of cellswere treated with a drug solvent mix comprising dimethyl sulphoxide, orDMSO, and phosphate buffered saline. It was assumed that the drugsolvent mix would not have an effect on miRNA expression, and if it did,it would not be consistent with any of the patterns associated with thedrugs being tested. However, the drug solvent mix was found to have amiRNA expression pattern consistent with an HDAC inhibitor.Subsequently, it was found from a literature review that DMSO had beenshown to be an HDAC inhibitor, confirming that unknown potentialtherapeutic properties of drugs can be determined using the methods ofthe invention.

Materials and Methods

HeLa cells were cultured using standard methods. The cells were splitinto DMEM medium.

The media was aspirated and the cell monolayer was washed with anappropriate amount of Phosphate Buffered Saline (PBS, 8 g NaCl, 0.2 gKCl, 1.44 g Na₂HPO₄ and 0.24 g KH₂PO₄ dissolved in 800 ml of distilledH₂O). The PBS was aspirated.

The test agent in question was administered to the cells and incubatedfor 48 hours.

RNA Extraction

RNA was isolated and purified from these cells using a column-based kitfrom Exiqon the following procedure.

The medium the cells were grown on was aspirated and the cell monolayerwas washed with an appropriate amount of PBS. The PBS was furtheraspirated.

350 μL of the lysis solution was added directly to a culture plate. Thecells were lysed by gently tapping the culture dish and swirling bufferaround the plate surface for five minutes. The lysate was thentransferred to a micro-centrifuge tube.

200 μL of 95-100% ethanol was added to the lysate and mixed by vortexingfor 10 seconds.

A column was assembled using one of the tubes provided in the kit. 600μL of the lysate/ethanol was applied onto the column and centrifuged for1 minute at 14,000×g. The flow-through was discarded and the spin columnwas reassembled with its collection tube.

400 μL of the supplied wash solution was applied to the column andcentrifuged for 1 minute at 14,000×g. The flow-through was discarded andthe spin column was reassembled with its collection tube.

The column was washed twice more by adding another 400 μL of washsolution and centrifuging for 1 minute at 14,000×g. The flow-through wasdiscarded and the spin column was reassembled with its collection tube.

The column was spun for two minutes at 14,000×g to thoroughly dry theresin and the collection tube was discarded.

The column was assembled into a 1.7 mL elution tube provided with kit.50 μL of elution buffer was added to the column and centrifuged for twominutes at 200×g followed by one minute at 14,000×g.

The resulting purified RNA sample could be stored at −20° C. for a fewdays. For long-term storage of samples were stored at −70° C.

(1) miRNA Microarray and Data Analysis

Labelling

Purified RNA samples were labelled using a labelling kit from Agilent.

The total RNA sample was diluted to 50 ng/μL in 1×TE pH 7.5. 2 μL of thediluted total RNA was added to a 1.5 mL micro-centrifuge tube and put onice. Immediately prior to use, 0.4 μL 10× calf intestinal phosphatasebuffer, 1.1 μL nuclease free water and 0.5 μL calf intestinalphosphatase were gently mixed to prepare a calf intestinal alkalinephosphatase master mix.

2 μL of the calf intestinal alkaline phosphatase master mix was added toeach sample tube for a total reaction volume 4 μL, and was gently mixedby pipetting. The reaction volume was incubated at 37° C. in acirculating water bath for 30 minutes.

2.8 μL of 100% DMSO was added to each sample. Samples were incubated at100° C. in a circulating water bath for 5-10 minutes and thenimmediately transferred to an ice bath.

10×T4 RNA ligase buffer was warmed to 37° C. and spun until allprecipitate had dissolved. Immediately prior to use, 1 μL of 10×T4 RNAligase buffer, 3 μL cyanine3-pCp and 0.5 μL T4 RNA ligase were gentlymixed to make a ligation master mix and put on ice.

4.5 μL of the ligation master mix was added to each sample tube for atotal reaction volume of 11.3 μL. Samples were gently mixed by pipettingand spun down. The samples were then incubated at 16° C. in acirculating waterbath for two hours. The samples were then dried using avacuum concentrator at 45-55° C. and the samples were determined to bedry if, when the tube was flicked the pellets did not move or spread.

Hybridization

125 μL of nuclease free water was added to the vial containinglyophilised 10×GE blocking agent supplied with the Agilent Kit andmixed.

The dried sample was resuspended in 18 μL of nuclease free water. 4.5 μLof the 10×GE blocking agent was added to each sample. 22.5 μL of2×Hi-RPM Hybridization buffer was added to each sample and mixed well.The resulting samples were incubated at 100° C. for 5 minutes, and thenimmediately transferred to an ice waterbath for a further 5 minutes.

A clean gasket slide was loaded into the Agilent SureHyb chamber baseensuring the gasket slide was flush with the chamber base. Thehybridization sample was dispensed onto the gasket well ensuring nobubbles were present.

An array was placed active side down onto the SureHyb gasket slide andassembled with the SureHyb chamber cover to form an assembled chamber.The assembled chamber was placed into a hybridization oven set at 55° C.and rotated at 20 rpm for 20 hours at that temperature.

The arrays were subsequently washed using the supplied GE wash buffersbefore being scanned.

(2) Quantitative Real-Time PCR Preparing the RT Reaction Master Mix

The components were thawed from frozen on ice. The RT reaction mastermix was prepared by mixing 0.15 μL dNTPs (100 mM), 1 μL MultiScribeReverse Transcriptase (MultiScribe is a trade mark of AppleraCorporation) (50 U/μL), 1.5 μL 10× Reverse Transcription Buffer, 0.19 μLRNase Inhibitor (20 U/μL), 4.16 μL nuclease-free water, and then storedon ice. Note that the volumes quoted above are per 15 μL RT reaction andwere scaled up for the number of RT reactions to be carried out.

Preparing the RT Reaction

For each 15 μL RT reaction, 7 μL RT master mix was combined with 5 μLtotal RNA. The RT Primers were thawed on ice and 3 μL of RT primer wasadded to the 12 μL of the RT master mix/total RNA in a 96-well platewell. The plate was kept on ice until filled and then put into thethermal cycler.

Thermal Cycler Steps:

-   -   16° C. for 30 minutes    -   42° C. for 30 minutes    -   85° C. for 5 minutes    -   4° C. for as long as convenient

PCR Amplification

For each well, 10 μL Taqman 2× Universal PCR Master Mix was mixed with7.67 μL nuclease-free water, 1 μL of 20× Taqman MicroRNA Assay mix and1.33 μL of the RT product from the previous step. When all the wellswere filled the plate was sealed with an optical adhesive cover andcentrifuged to remove any air bubbles.

The plate was then loaded into a real-time capable thermal cycler/PCRmachine and the following program followed:

-   -   95° C. for 10 minutes (Activation of the AmpliTaq Gold Enzyme)    -   40×(95° C. for 15 seconds, 60° C. for 60 seconds).

Data Analysis

Data from both of these techniques was normalised against the spike-inmiRNA spots for each plate, allowing data from separate arrays to becompared.

Normalised data was analysed using Principal Component Analysis, astandard technique well understood by those skilled in the art toidentify correlations between miRNA expression profiles, and anygrouping of data observed determined to be a consequence of the actionof the particular test agent applied to the original cells on theexpression of the individual miRNA.

FIG. 1 is a flow diagram of a method for obtaining an expression profilefor micro RNA.

FIG. 2 shows an example of an expression profile after principalcomponent analysis. Part (a) shows a three dimensional projection ofthree principal components of the total multidimensional expression dataset of miRNA expression and illustrates clustering of miRNA expressiondata for one treatment type. Part (b) shows the data spread for theexpression of single miRNA exp

FIG. 3 shows a statistical ranking of 11 discriminatory miRNAs labelledhas-miR-1 through has-miR-11. The p-value is the standard statisticaltest value of whether a result is statistically significant or theresult of chance (generally given to be a p-value of ≦0.05) and theq-value being the p-value corrected for multiple testing and provides ameasure of the false discovery rate. All p-values shown are much lessthan 0.05.

FIG. 4 (a) shows a projection of three principal components of themultidimensional data set for miRNA expression for multiple miRNAs andthe clustering of miRNAs indicative of a potential therapeuticapplications. (b) shows a projection of three principal components ofthe multidimensional data set for miRNA expression for multiple miRNAswherein the individual miRNAs are shaded to indicate the therapeuticapplication for which the biological intervention applied in theirexpression is used.

As can be seen, the results are clearly grouped and that this groupingis according to the therapeutic use of the biological interventionapplied to the cells in which the miRNAs were expressed. In other words,it is possible to determine that the grouped biological interventionsmay have a similar mechanism of action upon the cells to which they wereapplied, and the shared mechanism resulted in similar effects on miRNAsexpression levels.

Biological interventions with similar mechanisms of action may also havesimilar therapeutic properties and therefore they may have similartherapeutic applications. Data presented in FIGS. 3 and 4 demonstratesthat for the biological interventions tested, the projection of threeprincipal components of the multidimensional data set for miRNAexpression for biological interventions of similar therapeuticapplication (for example, anti-metabolites) did indeed group together,and that the groupings of biological interventions with a differenttherapeutic use (for example epigenetic modifiers) were groupedseparately.

A database of miRNA expression patterns can be built up by carrying outmany biological interventions and analysing the resulting changes inmiRNA expression profile. Such a database would enable identification ofthe therapeutic use, or potential future therapeutic use, of an untestedbiological intervention by comparing a miRNA expression profile of saiduntested biological intervention with that in the database anddetermining whether the said expression profile falls within one of thetherapeutic application groupings. If such a correlation occurs, theuntested biological intervention may be considered for that specifictherapeutic application.

Furthermore, building up a database of miRNA expression data may reveala subset of certain miRNAs that are indicative of a certain therapeuticapplication. Once said subset of indicative miRNAs are identified,future testing of new biological interventions to find potentialtherapeutic applications, or testing known biological interventions fornew therapeutic applications, can be carried out by looking at theexpression profiles of the subset of indicative miRNA expressionprofiles and not the entire range of miRNAs produced by the cells.

The database of miRNA expression data may also be employed to determinea subset of certain miRNAs, the expression levels of which are mostuseful for discriminating between biological interventions, or betweengroups of biological interventions known or hypothesized to have similarmodes of action. miRNAs may be ranked in order of the relevance of theirexpression levels for discriminating between biological interventions,or between groups of biological interventions known or hypothesized tohave similar modes of action. miRNAs may be allocated a numerical valueindicative of the relevance of their expression levels fordiscriminating between biological interventions, or between groups ofbiological interventions known or hypothesized to have similar modes ofaction. For example, the numerical value may be related to thecontribution of the expression level of an miRNA to the variance ofprinciple components.

As an alternative to, or in addition to, the comparison of miRNAexpression profiles using statistical methods such as principalcomponent analysis, the effect of a biological intervention on theexpression of each of a limited group of miRNAs (for example, 10-50) maybe identified and used to assign a code, selected from a group of codes,to the effect of the biological intervention on the expression of eachrespective miRNA. The resulting codes may be compared to identifysimilarities in effect.

For example, for each biological intervention (e.g. for each screenedcompound) a 3-digit binary number may be allocated as a code to eachranked miRNA based on:

1. If expression of the miRNA is unchanged (within normal limits ofexperimental variability) in response to the biological intervention,the first bit is set to 0. If expression has changed significantly, thefirst bit is set to 1.2. If a change in expression level was identified and the change was anincrease, the second bit is set to 1. If the change resulting from thebiological intervention was a decrease, the second bit is set to 0.3. If the change in expression level was more than 4-fold, the third bitis set to 1, otherwise it is set to 0.

Thus, the effect of a biological intervention level on the expression ofan miRNA is allocated a code having one of five possible values:

1. No change in expression—0002. Large increase in expression—1113. Small increase in expression—1104. Large decrease in expression—1015. Small decrease in expression—100

The effect of a biological intervention (for example, the administrationof a particular compound) on the expression level of a group of miRNAsmay be characterised by the associated code, permitting identificationof changes in expression level not immediately apparent from principalcomponent analysis, permitting alternative methods of scoring thesimilarity of biological interventions and rendering the resultingexpression data comprehensible by visual inspection.

Another way to characterise the effect of a biological intervention andto determine correlations between the effects on miRNA expression ofdifferent biological interventions is to carry out an expression assayto determine the effects of a biological intervention on the expressionof each of a group (of typically 10 to 50) miRNAs and to rank the miRNAsin that group in order of the effect, for example, in order from themiRNA in the group which has the largest increase in expression to themiRNA in the group which has the largest decrease in expression, or viceversa. The resulting rankings are indicative of the effects ofparticular biological interventions. Thus, the effect of otherbiological interventions on the group of miRNAs may be measured and themiRNAs in the group ranked in order of the effect. The resultingrankings may be compared to enable correlations between the effect ofbiological interventions to be identified.

A kit comprising plates operable to test the subset of indicative miRNAsmay be provided to significantly increase the efficiency and speed withwhich biological interventions can be screened for potential noveltherapeutic applications.

Further variations and modifications may be made within the scope of theinvention herein disclosed.

REFERENCES

-   1. Xie, X., et al., Systematic discovery of regulatory motifs in    human promoters and 3′-UTRs by comparison of several mammals.    Nature, 2005. 434(7031): p. 338-45-   2. Lim, L. P., et al., Microarray analysis shows that some microRNAs    downregulate large numbers of target mRNAs. Nature, 2005.    433(7072): p. 769-73-   3. Calin, G. A., et al., MicroRNA profiling reveals distinct    signatures in B cell chronic lymphocytic leukemias. Proc Natl Acad    Aci USA, 2004. 101 (32): p. 11755-60

1.-26. (canceled)
 27. An assay apparatus comprising miRNAs consisting ofa reduced group of miRNAs obtained by a method for identifying a groupof miRNAs for use in expression assays, the method comprising the stepsof: (1) carrying out a plurality of expression assays, each expressionassay comprising the steps of: carrying out at least one intervention ona biological system comprising cultured cells or ex vivo tissue,measuring an expression profile of a group of miRNAs in the biologicalsystem resulting from the at least one intervention, and storing anexpression data set derived from the measured expression profile, thesaid expression assays concerning a plurality of differentinterventions; (2) analysing the resulting expression data sets todetermine correlations between the effect on the expression profile ofmiRNAs of the at least one intervention in groups of two or moreexpression assays concerning the plurality of different interventions;(3) identifying a sub-group of the group of miRNAs having expressionprofiles on which the plurality of different interventions have acorrelated effect; and (4) selecting the sub-group of miRNAs for use infurther expression assays in which the expression profile of a reducedgroup of miRNAs is measured, the reduced group including only some ofthe group of miRNAs including at least the identified sub-group ofmiRNAs, wherein the at least one intervention comprises the applicationto the biological system of at least one of a group comprising: ionizingradiation; continuously emitted or pulsed electromagnetic radiation;acoustic energy; mechanical intervention; the application of pressure,magnetic fields, or electricity; changes in temperature; changes inosmolarity, tonicity or pH of a growth medium; changes in fluiddynamics; and mechanochemical signal transduction.